Abstract
Conventional data augmentation realized by performing simple pre-processing operations (e.g., rotation, crop, etc.) has been validated for its advantage in enhancing the performance for medical image segmentation. However, the data generated by these conventional augmentation methods are random and sometimes harmful to the subsequent segmentation. In this paper, we developed a novel automatic learning-based data augmentation method for medical image segmentation which models the augmentation task as a trial-and-error procedure using deep reinforcement learning (DRL). In our method, we innovatively combine the data augmentation module and the subsequent segmentation module in an end-to-end training manner with a consistent loss. Specifically, the best sequential combination of different basic operations is automatically learned by directly maximizing the performance improvement (i.e., Dice ratio) on the available validation set. We extensively evaluated our method on CT kidney tumor segmentation which validated the promising results of our method.
Original language | English |
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Title of host publication | 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1419-1423 |
Number of pages | 5 |
ISBN (Electronic) | 9781509066315 |
DOIs | |
Publication status | Published - 2020 May |
Externally published | Yes |
Event | 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain Duration: 2020 May 4 → 2020 May 8 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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Volume | 2020-May |
ISSN (Print) | 1520-6149 |
Conference
Conference | 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 |
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Country/Territory | Spain |
City | Barcelona |
Period | 20/5/4 → 20/5/8 |
Bibliographical note
Funding Information:The work was supported by the National Key Research and Development Program of China (2019YFC0118300), NSFC (61432008, 61673203, 81927808), and Jiangsu Provincial Key Research and Development Project (BE2018610).
Publisher Copyright:
© 2020 IEEE.
Keywords
- Data augmentation
- Deep reinforcement learning
- Medical image segmentation
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering